Identification and targeting of treatment resistant progenitor populations in T-cell Acute Lymphoblastic Leukemia

Refractoriness to initial chemotherapy and relapse after remission are the main obstacles to cure in T-cell Acute Lymphoblastic Leukemia (T-ALL). Biomarker guided risk stratification and targeted therapy have the potential to improve outcomes in high-risk T-ALL; however, cellular and genetic factors contributing to treatment resistance remain unknown. Previous bulk genomic studies in T-ALL have implicated tumor heterogeneity as an unexplored mechanism for treatment failure. To link tumor subpopulations with clinical outcome, we created an atlas of healthy pediatric hematopoiesis and applied single-cell multiomic (CITE-seq/snATAC-seq) analysis to a cohort of 40 cases of T-ALL treated on the Children’s Oncology Group AALL0434 clinical trial. The cohort was carefully selected to capture the immunophenotypic diversity of T-ALL, with early T-cell precursor (ETP) and Near/Non-ETP subtypes represented, as well as enriched with both relapsed and treatment refractory cases. Integrated analyses of T-ALL blasts and normal T-cell precursors identified a bone-marrow progenitor-like (BMP-like) leukemia sub-population associated with treatment failure and poor overall survival. The single-cell-derived molecular signature of BMP-like blasts predicted poor outcome across multiple subtypes of T-ALL within two independent patient cohorts using bulk RNA-sequencing data from over 1300 patients. We defined the mutational landscape of BMP-like T-ALL, finding that NOTCH1 mutations additively drive T-ALL blasts away from the BMP-like state. We transcriptionally matched BMP-like blasts to early thymic seeding progenitors that have low NR3C1 expression and high stem cell gene expression, corresponding to a corticosteroid and conventional cytotoxic resistant phenotype we observed in ex vivo drug screening. To identify novel targets for BMP-like blasts, we performed in silico and in vitro drug screening against the BMP-like signature and prioritized BMP-like overexpressed cell-surface (CD44, ITGA4, LGALS1) and intracellular proteins (BCL-2, MCL-1, BTK, NF-κB) as candidates for precision targeted therapy. We established patient derived xenograft models of BMP-high and BMP-low leukemias, which revealed vulnerability of BMP-like blasts to apoptosis-inducing agents, TEC-kinase inhibitors, and proteasome inhibitors. Our study establishes the first multi-omic signatures for rapid risk-stratification and targeted treatment of high-risk T-ALL.

Oncology Group AALL0434 clinical trial.The cohort was carefully selected to capture the immunophenotypic diversity of T-ALL, with early T-cell precursor (ETP) and Near/Non-ETP subtypes represented, as well as enriched with both relapsed and treatment refractory cases.Integrated analyses of T-ALL blasts and normal T-cell precursors identi ed a bone-marrow progenitor-like (BMP-like) leukemia sub-population associated with treatment failure and poor overall survival.The single-cell-derived molecular signature of BMP-like blasts predicted poor outcome across multiple subtypes of T-ALL within two independent patient cohorts using bulk RNA-sequencing data from over 1300 patients.We de ned the mutational landscape of BMP-like T-ALL, nding that NOTCH1 mutations additively drive T-ALL blasts away from the BMP-like state.We transcriptionally matched BMP-like blasts to early thymic seeding progenitors that have low NR3C1 expression and high stem cell gene expression, corresponding to a corticosteroid and conventional cytotoxic resistant phenotype we observed in ex vivo drug screening.To identify novel targets for BMP-like blasts, we performed in silico and in vitro drug screening against the BMP-like signature and prioritized BMP-like overexpressed cell-surface (CD44, ITGA4, LGALS1) and intracellular proteins (BCL-2, MCL-1, BTK, NF-κB) as candidates for precision targeted therapy.We established patient derived xenograft models of BMP-high and BMP-low leukemias, which revealed vulnerability of BMP-like blasts to apoptosis-inducing agents, TEC-kinase inhibitors, and proteasome inhibitors.Our study establishes the rst multi-omic signatures for rapid risk-strati cation and targeted treatment of high-risk T-ALL.

Main Text
Acute lymphoblastic leukemia (ALL) is the most common pediatric cancer and leading cause of pediatric cancer mortality 1 .Outcomes in B-ALL have improved drastically due to optimization of chemotherapy, development of targeted therapies, and genetically-guided risk strati cation.In contrast, while outcomes have improved in T-ALL, most patients who relapse are considered low/favorable risk at diagnosis and few targeted therapies have successfully translated into the clinic.Outcomes for patients with relapsed T-ALL are dismal as there are no effective salvage options.Accordingly, T-ALL is a disease where the goal is to use the most effective therapy at initial diagnosis.There is an urgent need to identify biologic risk factors in T-ALL to inform development of targeted therapeutics and enable early identi cation of highrisk patients who need alternative strategies for cure 2 .
Bulk genomic study of T-ALL has implicated tumor heterogeneity as a possible mechanism of treatment failure; however, the phenotype of treatment-resistant tumor subpopulations remains unde ned.Here, we used single-cell multi-omics to map the tumor landscape of >600,000 T-ALL blasts to the full hierarchy of pediatric hematopoiesis.We identify and characterize a chemo/steroid-resistant bone marrow progenitorlike (BMP-like) tumor population shared between high risk patients across the immunophenotypic spectrum of T-ALL.We used single-cell multiomics, large-cohort bulk genomics, and primary patient xenograft models to establish multi-omic signatures for rapid risk assessment and test novel sensitivities of BMP-like blasts which can be leveraged by currently available targeted therapeutics.

Subpopulation level overlap of developmental arrest spectra in immunophenotypically distinct T-ALLs
We studied 25 ETP, 5 Near-ETP, and 10 Non-ETP T-ALL patients with varied clinical response from treatment on AALL0434, an international phase 3 randomized Children's Oncology Group (COG) clinical trial that reported the best published outcomes for children and young adults with T-ALL (Fig. 1a).We selected patients who quickly went into minimal residual disease (MRD) negative remission and were cured (n=16), patients who had intrinsic chemotherapy resistance (refractory, enriched for cases with induction failure; n=10), and patients who relapsed (n=14) (Supplementary Table 1).Our samples were broadly representative of AALL0434 cases at the clinical co-variate (Supplementary Table 2) and bulktranscriptomic levels (Fig. 1b), suggesting that our single cell study is well poised to add depth to wholecohort bulk genomic sequencing across all three immunophenotypic subtypes.We performed Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq) and single cell Assay for Transposase-Accessible Chromatin (ATAC) sequencing (scATAC-seq) on live cells sorted from cryopreserved diagnostic BM aspirate (n=32) or peripheral blood mononuclear cells (PBMCs, n=8), capturing over 600,000 high-quality cells (Supplementary Data Fig 1b-j, Supplementary Table 3-4) across both modalities (Fig. 1c, Supplementary Data Fig 2a-i).To robustly phenotype our single cell dataset within the context of normal hematopoietic development, we assembled a multiomic reference map of healthy pediatric hematopoiesis, using normal thymus (Supplementary Table 5) and bone marrow (Supplementary Table 6) tissues collected from children (Fig 1d, Extended Data Fig 1a-h).
We directly mapped T-ALL blasts to the full hierarchy of human hematopoietic development, overcoming the limitations of using bone marrow restricted references 3 or mouse derived thymocyte signatures 4 (Fig. 1e).To assess the integrity of our reference mapping method, we additionally projected scRNA-seq and scATAC-seq data from 10 AML and 10 T-Myeloid Mixed Phenotype Acute Leukemia (T/M MPAL) samples onto our reference, nding that AML blasts projected to the monocytic lineage and T/M MPAL blasts projected to both monocytic and T-cell lineages, as expected.Notably, all ve subtypes of leukemia showed a spectrum of developmental arrest states with signi cant overlap at the subpopulation level.Arrest in a multipotent progenitor-like (HSPC/LMPP) state represented a shared cell state in ETP-ALL, T/M MPAL, and AML; Pro-T-like arrested blasts were shared between T/M MPAL and all three subtypes of T-ALL, and Pre-T-like arrested blasts were shared between Near-ETP and Non-ETP T-ALL (Fig. 1f).
A primary hypothesis for treatment resistance in ETP-ALL has been that ETP-ALL retains myeloid populations that confer resistance to ALL-directed therapy.We found the median frequency of myeloidprojecting blasts (GMP, DC-Progenitor, pDC, cDC, or Mono) to be 0.167% in ETP-ALL patients, in contrast to 16% and 82.5% in T/M MPAL and AML patients, respectively (Fig. 1f).GMP-like and monocytic-like populations comprised <1% of blasts in 18/25 of ETP-ALL patients, and were not detected in 5/25 patients: strongly supporting a lymphoid-progenitor origin of ETP-ALL blasts and use of ALL-directed therapy in ETP-ALL.Surprisingly, the developmental arrest spectrum of Near-ETP T-ALL was much closer to Non-ETP T-ALL, an unexpected nding given that Near-ETP T-ALL is de ned by a stem/myeloid ETP immunophenotype with the exception of high (>75%) CD5 expression.To further assess whether the divergence of ETP and Near-ETP developmental arrest spectra could explain diverging clinical responses of Near-ETP and ETP cases observed within AALL0434 (Extended Data Fig. 2a), we performed differentiation expression (DE) and differential chromatin accessibility (DA) analyses (Supplementary Figure 3a-d).
We observed Near-ETP blasts to have downregulation of stem/myeloid markers (SPINK2, C1QTNF4, HLA-DRA) and upregulation of T-cell receptor (TCR) related machinery (LAT, CD3E, CD28, LCK, PTCRA) as compared to ETP-ALL blasts, in line with commitment to the T-lineage (Supplementary Data Fig 3b).In addition, Near-ETP blasts had increased expression and motif accessibility of TCF7 and LEF1 (Extended Data Fig. 2b), two TFs central to T-lineage commitment in healthy thymocytes.Within healthy donor scRNA and scATAC-seq, we observed expression and accessibility of TCF7/LEF1 to rise at the Pro-T stage and peak at the Pre-T stage (Extended Data Fig. 2c .Our analysis also predicted direct regulation of CD5 expression and TCRrelated genes (LAT, DNTT, MAL, CD3E) by TCF7/LEF1 in Near-ETP blasts, connecting TCF7/LEF1 regulation to the CD5-bright phenotype observed within diagnostic ow cytometry (Extended Data Fig. 2e).Elevated expression of our predicted TCF7/LEF1 regulon was consistently observed in bulk-RNA-seq data of n=110 and n=168 ETP and Near-ETP patients, respectively (Extended Data Fig. 2f, Supplementary Data Fig 4d).Notably, ETP and Near-ETP patients with higher expression of the TCF7/LEF1 signature had more favorable outcomes within AALL0434 (Extended Data Fig 2g-i), with TCF7/LEF1 signature having prognostic signi cance independent of MRD and CNS status in ETP-ALL patients (92.7% vs 79.3% 5-year OS, p = 0.024, Extended Data Fig. 2h).Taken together, our results suggest that TCF7/LEF1 activation underlies improved clinical performance within immature (ETP/Near-ETP) T-ALL, highlighting complex and functionally signi cant transcriptional regulatory circuits that underlie minute, single-marker immunophenotypical differences.
Treatment resistance in ETP-ALL is associated with a progenitor-like population High degrees of initial treatment resistance, rather than eventual relapse, contribute to poor outcome in ETP-ALL 5 .Within AALL0434, ETP-ALL patients were 7.1-fold less likely to achieve remission (<5% blasts by morphology on post-induction bone marrow aspirate) after the rst month of chemotherapy and more than twice as likely to have MRD detectable by ow cytometry at end of induction (Day 29, EOI) compared to Non-ETP patients.Intrinsic steroid resistance has been suggested to play a central role in treatment resistant ETP-ALL, but the phenotype and mechanism of resistant tumor populations remain unclear.
We enriched our single-cell cohort for treatment refractory ETP cases to identify tumor cell states associated with initial treatment resistance.To this end, we rst compared the initial developmental arrest state of 10 ETP patients with high end-induction MRD (EOI MRD >20%) to 10 ETP patients that were EOI MRD negative (<0.1%) (Supplementary Data Fig. 5a-e).We rst asked if treatment response to chemotherapy was simply correlated with the fraction of actively cycling tumor cells.Although we observed a small enrichment of cycling cells in MRD negative patients (20% vs 16%, Supplementary Data Fig 5f-g), we observed all treatment refractory patients to have signi cant proportions of cycling cells, prompting us to further investigate whether treatment response could be better explained by differences in cell arrest state.Within scRNA and scATAC-seq data, we observed that ETP patients with EOI MRD > 20% had an enrichment of blasts at HSPC/LMPP/CLP/ETP developmental stages (Fig. 2a, Supplemental Data Fig. 5h).Multi-lineage potency is retained 6,7 in these states, thus, we termed this cell state bonemarrow-progenitor-like ("BMP-like").In contrast, ETP patients that were MRD-negative had an enrichment of blasts in the Pro-T/Pre-T stage.These states represent speci cation to the T-lineage and we henceforth refer to them as "T-speci ed."We observed the proportion of tumor blasts in BMP-like and T-speci ed developmental stages to associate with Day 29 centrally assessed risk (Fig. 2a), overall survival (OS; Fig. 2c), and event free survival (EFS; Supplementary Data Fig. 5i-j) in single-cell sequenced ETP-ALL patients (n=25).
Previous single cell studies have suggested that a portion of thymic seeding progenitors (TSPs) to be more stem-like than others -retaining similar markers of multipotency seen in BMP-like ETP blasts 16 .In our analysis, we transcriptomically matched BMP-like ETP-ALL blasts to the earliest, most stem-like subset of TSPs (Extended Data Fig. 3a-c), revealing two putative mechanisms of treatment resistance.Firstly, we found corticosteroid receptor (NR3C1) expression to be directly correlated with T-cell differentiation state (Extended Data Fig. 3d).In line with the major role of corticosteroids in ALL induction therapy, NR3C1 expression was highly predictive of EOI MRD (Extended Data Fig. 3e).BMP-like blasts had signi cantly lowered expression of NR3C1 (Extended Data Fig. 3d), rendering them highly resistant (>80-fold increase in IC50) to prednisolone within in-vitro dose response experiments (Extended Data Fig. 3f).We also predicted that BMP-like blasts would have high self-renewal capacity, much like TSPs.Indeed, we observed upregulation of leukemic stem-cell related transcriptional programs in BMP-like blasts (Extended Data Fig. 3g), associating with >100-fold resistance to T-ALL induction agents within in vitro testing (Extended Data Fig. 3h).

BMP-like and T-speci ed states are associated with distinct driver mutations
Given the divergent phenotypes observed within BMP-like and T-speci ed ETP blasts, we next wondered if these two cell states represented acquisition of different driving mutations.To address this question, we leveraged the intersection of single-cell derived phenotypes with structural variant (SV) and single nucleotide variant (SNV) calls (Supplementary Table 11) to identify associated drivers of BMP-like and Tspeci ed cell states (Fig. 3a-b, Supplementary Data Fig. 7a-e).While BMP-like leukemias harbored fusion products known to drive high HOXA cluster expression, including MLLT10, KMT2A, NUP214, and direct HOXA::TCR fusions, T-speci ed leukemias had fusions to ZFP36L2 (involved in cell cycle control during Tcell beta selection) and TLX1/3.Notably, deaths from disease within the AALL0434 trial (n=110 ETP patients) were all patients with BMP-like associated fusions (Fig. 3a).On the SNV level (Fig. 3b), BMP-like high patients had recurrent mutations in TF and signaling pathways (ie, JAK3, NRAS, WT1, ETV6 and SATB1) while T-speci ed high patients had mutations in T-lineage regulators (ie, IL7R, NOTCH1, and RUNX1).Top BMP-like associated SNVs were associated with inferior outcome (Extended Data Fig. 4a-b, Supplementary Data Fig. 8a-d), while top T-speci ed SNVs showed the opposite trend (Extended Data Fig. 4c, Supplementary Data Fig. 8e-h).In line with its essential role in T-lineage commitment, the most recurrently altered gene associated with either cell state was NOTCH1.Within our single-cell cohort, we observed that NOTCH1 mutant patients (n=6) had divergent arrest spectra compared to NOTCH1 WT (n=19) patients (Fig. 3c), with a notable depletion of BMP-like blasts (Fig. 3d).Interestingly, of patients that were treatment sensitive (EOI MRD <0.1%) with BMP-like associated drivers (KMT2A and MLLT10 fusions), 2 out of 3 harbored tumors with multiple activating NOTCH1 mutations (Extended Data Fig. 5a), suggesting that pan-tumor NOTCH1 activation can drive differentiation away from the high-risk BMP-like state.This led us to ask whether NOTCH1 activation represented a universal marker of treatment sensitivity within the larger bulk-sequenced cohort.Of 110 bulk-sequenced ETP-ALL patients, 41 harbored NOTCH1 mutations, with 18 having 2+ mutations (range: 2-5).Patients with 2+ NOTCH1 mutations had higher T-speci ed signature scores (Fig. 3e), aligning with an elevated NOTCH1 variant allele fraction (>50% vs <50%) (Extended Data Fig. 5b).Remarkably, all 18 patients with 2+ NOTCH1 mutations in the bulk-sequenced cohort were alive at last known follow-up, outperforming patients with single NOTCH1 mutation and NOTCH1-wt patients (Fig. 3f).
To study NOTCH1-mutant subclones at the single-cell level, we performed genotyping of transcriptomes (GoT) on two patients harboring a total of 7 unique activating NOTCH1 mutations (Extended Data Fig. 5a-c, Supplementary Data Fig. 9a-d).We successfully detected 7/7 NOTCH1 mutations in scRNA-seq libraries, corroborating bulk-derived variant calls (Extended Data Fig. 5d, Supplementary Data Fig. 9e-g).In strong support of the observations gleaned from bulk data, we found that NOTCH1-mutant cells were predominantly in the T-speci ed, rather than BMP-like cell state (Extended Data Fig. 5e).Remarkably, we discovered hundreds of leukemic blasts that carry 2 co-occurring NOTCH1 mutations, likely resulting from selection for NOTCH1 mutation in distinct alleles (Extended Data Fig. 5f).We found blasts with 2 unique mutations to have the highest expression of T-speci ed genes and lowest expression of BMP-like genes (Extended Data Fig. 5g), revealing a direct connection between NOTCH1 mutation dosage and the Tspeci ed cell state (Extended Data Fig. 5h).Taken together, these data cement NOTCH1 mutation status as critical biomarker for response to conventional therapy and offer high-resolution insight as to how NOTCH1 mutations alter T-ALL cell state.

BMP-like sub-populations predict outcome in immunophenotypically mature T-ALL
Relapsed/refractory T-ALL is nearly universally fatal due to acquired chemo-resistance and lack of targeted therapy options.Given that BMP-like blasts are highly resistant to conventional T-ALL therapy, we wondered if an analogous, less differentiated subpopulation could be responsible for treatment resistance and relapse in Non-ETP T-ALL.Analysis of bulk-RNA sequencing data from the AALL0434 Non-ETP cohort support this hypothesis, with differential expression between 355 MRD+ and 714 MRD-Non-ETP T-ALL cases (Supplementary Table 12) revealing gross differences in differentiation state (Supplementary Data Fig. 10a).Within this analysis, MRD-negative patients overexpressed markers of the α/β stage (CD1B, CD1E, MAL, CD8A, PTCRA, RAG1/2), while MRD+ patients expressed immature forms of the TCR (TRGC1/2) and stem-related TFs (HHEX, LYL1).We utilized single-cell multiomics data to determine whether these differences were mediated by cell state or cell proportion differences.
We strati ed our 10 Non-ETP patients into two groups: 6 patients with complete response (CR; EOI MRDnegative), and 4 patients that were EOI MRD+ (EOI MRD > 0.1%).Within both scRNA and scATAC projections to reference, we observed that non-CR patients had an enrichment of cell states prior to T-cell commitment (Pro-T, CLP, LMPP, MEP, HSPC), while CR patients had an enrichment of cells in postcommitment states (DP, α/β) (Fig. 4a, Supplementary Data Fig. 10b).Since pre-committed blasts represent a continuum of cell states from BMP-like to Pro-T-like, we compared the distribution of precommitted blasts in CR patients with non-CR patients.We found that MRD+ patients harbored a strong enrichment of BMP-like blasts, which were near absent in CR patients (Fig. 4b).We next performed differential expression analysis and generated signatures (Supplementary Table 13-14) for both precommitted non-ETP blasts and BMP-like non-ETP blasts (Supplementary Data Fig. 10c-d).We found that these signatures were both able to stratify Non-ETP patients from two independent COG trials (AALL0434, full cohort sequenced; AALL1231, partially sequenced cohort) independent of MRD, with the BMP-like signature having slightly better strati cation in both cases (Supplementary Data Fig. 10e-f).
We next intersected the molecular signature of BMP-like blasts obtained from ETP-ALL and Non-ETP T-ALL patients, revealing a shared BMP-like gene set ("BMP-17") composed of 17 marker genes (Fig. 4c, Supplementary Table 15) typically expressed in stem, myeloid, and B-cell progenitors (Fig. 4d, Extended Data Fig. 6a).We applied BMP-17 to ve different clinical scenarios for risk strati cation of T-ALL patients (Fig 4c), observing robust risk-strati cation in all instances (Fig. 4e-i).BMP-17 powered risk strati cation within the smaller, partially sequenced AALL1231 cohort (Fig. 4e), and was prognostic independent of EOI MRD and EOI CNS status within the fully sequenced AALL0434 cohort (Fig. 4f), including when patients were strati ed by ETP status (Fig. 4g-i).Using single-cell antibody derived tag (ADT) values obtained from CITE-seq, we next determined the surface immunophenotype of BMP-like blasts, revealing a 9-marker phenotype ("BMP-surface-9", Extended Data Fig. 7a, Supplementary Table 16) that re ected similar lineage-aberrancy (Extended Data Fig. 6b) observed with BMP-17.Because our ADT panel detects of 22 surface epitopes used in the ow cytometric diagnosis of T-ALL, we reasoned that the surface phenotype of BMP-like blasts could be highly clinically actionable.Risk strati cation using BMP-surface-9 genes in bulk-RNA-sequenced patients lend strong support to this hypothesis, with RNA expression of BMP-surface-9 genes robustly powering risk strati cation within the AALL0434 and AALL1231 T-ALL cohorts (Extended Data Fig. 7b-e).We additionally validated the BMP-surface-9 in n=99 AALL0434 diagnostic ow cytometry cases (Extended Data Fig. 7g-i), nding robust correlation of clinically used surface markers with the BMP-17 gene signature.We next turned out attention towards non-pediatric T-ALL, which is enriched for both treatment refractory cases and ETP phenotype.To test whether our BMP-subpopulation signatures could be extended to scenarios of non-pediatric T-ALL, we applied BMP-17 to young-adult (age > 18) cases treated on AALL0434: successfully isolating a subset of high-BMP cases (Extended Data Fig. 8a-c) with high rates of EOI MRD/induction failure (Extended Data Fig. 8d-e) and reduced OS and EFS (Extended Data Fig. 8f).These results highlight the prognostic utility of immunophenotype-based detection of high-risk BMP-like blasts, revealing a novel, subpopulationbased use case for clinical ow cytometry data.Additionally, our results highlight the potential prognostic utility of BMP-like blasts in both pediatric and non-pediatric settings and underline the utility of single-cell methods to uncover mechanisms of treatment resistance seen exclusively at sub-population level.
In silico and in vitro drug screening identify BCL2 and BTK dependency in BMP-like blasts The universal existence of BMP-like populations across treatment refractory T-ALL cases prompted us to develop a pipeline for development of BMP-like directed targeted therapy.To support the modeling of BMP-like therapy response, we rst expanded blasts from 22 single-cell sequenced patients in NSG mice (Extended Data Fig. 9a-c).Single cell RNA-sequencing on engrafted blasts from 16 patients indicated strong retention of patient-speci c features, with most PDX-engrafted blasts projecting directly adjacent to their patient source (Fig. 5a).In addition, we found that BMP-high patients maintain BMP-high phenotypes post engraftment (Fig. 5b), aligning with our observation that BMP-high leukemias engrafted at slightly higher e ciency (Extended Data Fig. 9d-e).
Having identi ed a putative driver of resistance in these patients, we next considered how to leverage this as a potential therapeutic vulnerability.We thus performed computational screening for targets that would be druggable and speci c to BMP-like blasts from treatment resistant patients.We queried 552 BMP-like speci c genes against three drug target databases (TTD, DrugIDB, OpenTargets, Extended Data Fig. 10a), one transcriptomic-based compound screening database (LINCS1000, Extended Data Fig. 10b), and one cancer gene vulnerability database (DepMap, Extended Data Fig. 10c).The consensus results from our 5-database approach (Supplementary Table 17, Extended Data Fig. 10d) nominated four druggable surface proteins (CD44, LGALS1, ITGA4, CD74), three homeostatic enzymes (S100A4, BCL2, HSP90), two signal transduction molecules (SYK, BTK), and one transcription factor (BCL11A) (Fig. 5c).
To test these computational predictions, we performed in vitro drug screening using an established panel of 40 leukemia active drugs (Fig. 5d, Supplementary Table 18).PDX-expanded blasts from 5 patients were screened using a stromal cell co-culture system and dose response curves were generated for each compound (Supplementary Table 19).Out of 40 compounds, 9 compounds were active in all PDX models (Extended Data Fig. 10e).These compounds had different activity across BMP-high patients (n=3) and BMP-low patients (n=2).While BMP-high/MRD+ patients had increased sensitivity to venetoclax, navitoclax, and ibrutinib, BMP-low/MRD-patients were more sensitive to conventional cytotoxics (prednisolone, clofarabine, daunorubicin) (Fig. 5e-f).Lowered expression of the steroid receptor (Supplementary Data Fig. 5d), and upregulation of leukemic stem-cell related transcriptional programs (Supplementary Data Fig. 5g) in BMP-like blasts provide potential explanations for >80-fold and >100fold resistance to corticosteroids and chemotherapy, respectively.BCL2 (5 database hit) and BTK (4 database hit) were top predicted BMP-like vulnerabilities based on our 5-database approach (Extended Data Fig. 10d).The strong in vitro activity of apoptosis-inducing agents against BMP-like blasts prompted us to initiate in vivo e cacy studies in BMP-high (PATTDP, n=6) and BMP-low (PAUNDK, n=9) PDX models (Extended Data Fig 9f).After 4 weeks of venetoclax treatment, we performed ow cytometry to quantify the residual leukemic burden in both bone marrow and spleen (Extended Data Fig. 9f).In the peripheral blood, venetoclax treatment resulted in halting of disease progression in BMP-low models compared to control (Supplementary Data Fig. 11a-c).However, after the conclusion of the study, BMP-low PDX models still harbored signi cant residual disease within both the bone marrow (>38% blasts) and spleen (>4% blasts) (Extended Data Fig. 9g, right, Supplementary Data Fig 12a-b).In contrast, venetoclax treatment resulted in robust clearance of disease in BMP-high PDX (Extended Data Fig. 9g, left, Supplementary Data Fig. 11d-f), with reduction of blasts to beyond our limit of detection (<0.01%) the majority of PDX (Supplementary Table 20).We found these robust responses to be present in both the bone marrow and spleen (Extended Data Fig. 9h) and easily detected by hCD7+/hCD45+ immunophenotypic pro ling (Supplementary Data Fig. 13a-b).Our results support a new treatment paradigm for T-ALL involving: (1) early genetic screening for the chemotherapy-refractory BMPlike phenotype and (2) clinical testing of BCL2 inhibitors in refractory BMP-like T-ALL.

Discussion
Our study reports the rst comprehensive mapping of T-ALL to healthy human hematopoiesis.We report the surprising discovery that T-cell leukemias differing drastically by bulk-immunophenotype are linked at the subpopulation level.Our integrated analysis identi es a shared BMP-like population tightly associated with treatment failure in ETP, Near-ETP, and Non-ETP T-ALL.This subpopulation can represent <5% of blasts at diagnosis, illustrating the limitations of current bulk-level tumor classi cation schemes.
Interestingly, BMP-like blasts are also found in myeloid and mixed phenotypic leukemias, raising the possibility of one common cell of origin.Evidence supporting this includes an enrichment of TF and signaling gene mutations within BMP-like T-ALL blasts (a mutational spectrum shared with myeloid leukemias), non-T lineage marker expression, and shared drug sensitivity pro les with myeloid leukemia stem cells.The unique opportunity to intersect our single-cell data with large-cohort bulk WES/WGS data allowed us to the associate BMP-like phenotype and genotype at subclonal resolution.Our data implicating the inverse relationship between NOTCH1 activation and the BMP-like state represents a potential therapeutic strategy in T-ALL and provide rationale for NOTCH1 activation, rather than inhibition, to treat high-risk T-ALL.Although our study is heavily focused on pediatric T-ALL, our ndings are perhaps even more relevant in the adult setting, where the ETP phenotype represents up to 52% of cases and 5year survival rates are <50%.Detection of high-risk cases within young adults treated on AALL0434 using BMP-like signatures supports the hypothesis that BMP-like blast mediated treatment failure extends beyond pediatric T-ALL cases.Interestingly, within ECOG2993, one of the largest adult T-ALL cohorts genomically studied to date, NOTCH1-mutation status conferred a signi cant survival advantage, further supporting a common mechanism of treatment resistance mediated by NOTCH1-wt, BMP-like subpopulations.Further genomic and experimental studies should be rapidly explored to con rm the phenotype and prognostic utility of BMP-like blasts within adult T-ALL cases.Finally, our single-cell multiomic reference maps of human hematopoiesis represents a valuable resource to further delineate the impact of developmental heterogeneity in human leukemia.Utilization of these references maps within ve subtypes of acute leukemia underlined a BMP-like arrest state shared between lymphoid, myeloid, and mixed phenotype leukemic disease, highlighting opportunity for further study.For instance, these reference maps and gene signatures could be utilized to study tumor evolution in the context of relapsed tumors and serial samples.Collectively, our study identi es a rare but clinically important BMPlike subpopulation which represents a promising therapeutic target in the continuing effort to rescue relapsed/refractory T-ALL patients from their currently dismal prognosis.Single-cell approaches on a carefully selected cohort were uniquely powered to isolate the BMP-like gene signature for risk strati cation and therapeutic targeting: illustrating a prime example where high-resolution single-cell analyses are needed to supplement high-throughput bulk genomic approaches for understanding clinically relevant tumor biology.

AALL0434 Patient Identi cation and Clinical Annotation
Children's Oncology Group (COG) studies (NCT04408005) and AALL1231 (02112916) were approved by the NCI Cancer Evaluation and Therapeutic Program (CTEP), the FDA, the Pediatric Central Institutional Review Board (IRB), and by local IRBs at all participating centers.Written informed consent/assent was obtained from study participants and, when appropriate, their legal guardians, in accordance with the Declaration of Helsinki.
Secondary genomic studies were approved by the Children's Hospital of Philadelphia (CHOP) IRB.40 cases from AALL0434 (Supplementary Table 1) and 8 healthy thymus and bone-marrow controls (Supplementary Table 5-6) were selected for single-cell study.Within AALL0434, ETP status was centrally assessed in diagnostic bone marrow (BM) or peripheral blood (PB) samples by 8-9 color multiparameter ow cytometry. 26ETP was de ned as having lymphoblasts that were CD8negative and CD1anegative (<5% positive), weakly expressed CD5 (either < 75% positive or median intensity more than 1 log less than mature T cells), and expressed one or more myeloid or stem cell markers (>25% positive) including CD13, CD33, CD34, CD117 or HLA-DR. 13Subjects meeting the ETP immunophenotypic criteria, but with stronger expression of CD5 were classi ed as Near-ETP.Subjects with neither ETP nor Near-ETP were de ned as Non-ETP.MRD was assessed using 8 and 9 color ow cytometry and was performed using established methods at a COG ow cytometry reference laboratory (University of Washington-BLW; or Johns Hopkins University-MJB).

Processing of T-ALL diagnosis samples for single cell assays and injection to PDX
Expansion of T-ALL blasts in PDX and post-engraftment scRNA/CITEseq PDX expanded blasts were harvested from spleen or bone marrow.Frozen samples were thawed (37°C) and resuspended in IMDM + 2% FBS and treated with DNase I twice.Cells were gently washed 2x with RPMI, resuspended in ow buffer, stained with DAPI and anti-human CD45 antibody (BD Pharmingen, Cat #: 555485) and subject to FACS sorting (FACSAria Fusion, BD).DAPI-hCD45+ sorted cells were stained with 10x Genomics 3' CellPlex multiplexing solution, washed 3x, and immediately processed using the 10x Genomics Chromium controller and the Chromium NEXT GEM Single Cell 3′ reagent kits (V3.1).3′ GEX libraries were constructed using 10x Genomics library preparation kit.CellPlex libraries were constructed using 10x Genomics 3' CellPlex kit.Library quality was checked using Agilent High Sensitivity DNA kit and Bioanalyzer 2100.Libraries were quanti ed using dsDNA High-Sensitivity (HS) assay kit on Qubit uorometer and the qPCR-based KAPA quanti cation kit.Libraries were sequenced on an Illumina Nova-Seq 6000 with 28:8:0:87 paired-end format.
CD34+ progenitor isolation from infant/pediatric thymi were obtained and used according to and with the approval of the CHOP IRB.Thymus tissue was mechanically disrupted and treated with liberase (0.2mg/mL Roche; 30 min at 37°C) with intermittent shaking, as previously described 17 .Thymocytes were resuspended into ow buffer, sorted into DAPI-Lin-CD34+CD1A-fractions and subject to scRNA-seq and scATAC-seq.The antibodies used for cell sorting described above were listed in Supplementary Methods.

Projection of patient scRNA and scATAC data onto healthy reference trajectory
Patient derived cells were projected onto the healthy reference trajectory using the MapQuery function in Seurat v4.0.5 18 , which assigns (1) predicted reference UMAP coordinates (2) nearest reference cell-type label, and (3) con dence score for nearest reference cell-type labels.In addition to default cell type label transfer, predicted T-cell trajectory pseudotime values and Myeloid-trajectory pseudotime values were transferred.To order myeloid development and T-lineage development onto a single trajectory, pseudotime values underwent the same transformations as described for healthy reference data.
For scRNA-seq data, the UMAP model was constructed with the top 25 principal components and the min.distparameter of 0.5 using uwot 0.1.10function.Next, patient and healthy control data were coembedded into a low-dimensional space using the default anchor-based CCA method in Seurat 4.0.5 (30 dimensions, 2,000 anchor features), and cell type label transfer was performed on a sample-by-sample basis using the TransferData function.
For scATAC-seq data, the UMAP model was constructed using uwot 0.1.10with dimensions 2 to10 from latent semantic index based reduction (LSI) and the min.distparameter of 0.5.Prior to anchor-based integration and label transfer, peaks from patient and healthy reference data were merged using the mergePeaks module from scATAC-pro 19 , and peak-by-cell matrices with merged peaks were reconstructed for each patient with the scATAC-pro reConstMtx module.This allowed for patient and healthy control data to be co-embedded into a low-dimensional space using the default anchor-based CCA method in Seurat 4.0.5 (30 dimensions, 2,000 anchor features), and cell type label transfer to be performed on a sample-by-sample basis using the TransferData function.

Quanti cation of developmental arrest state
Two independent visualizations -heatmap (plotting the distribution of blasts along pseudotime) and boxplot (plotting the proportion of blasts in any de ned cell state) -were used to delineate the arrest pro le of T-ALL blasts.T-ALL blasts were subset from non-malignant cells, leaving 250,911 and 261,500 cells for analysis in CITE-seq and scATAC-seq datasets, respectively.Heatmaps of T-ALL blast arrest were used to visualize the overall arrest state of blasts and were plotted with the following parameters: predicted pseudotime (myeloid from -1 to 0; T from 0 to 1) as the x-axis, comparator variable (ETP status, MRD status, risk group, etc) as the y-axis, proportion as the ll, 20 bins.Above each heatmap, cell types from healthy donor scRNA/scATAC-seq trajectories were plotted with the following parameters: pseudotime (myeloid from -1 to 0; T from 0 to 1) as the x-axis, frequency as the y axis.Statistical comparisons in overall arrest state were made using two-sample Kolmogorov-Smirnov (KS) test, as previously described 20 .
Boxplots were used to inspect patient-level arrest proportions in key cell states learned from heatmap visualizations.Each dot represents one patient and each panel one cell state.Patients were dichotomized by comparator variable (ETP status, MRD status, OS, EFS, etc).Statistical comparisons were made using Wilcoxon rank-sum test, unless otherwise speci ed in the gure legend.Integration of single cell signatures with bulk WES/WGS/RNA-seq derived mutation calls Bulk-RNAseq data for n=110 ETP samples with corresponding WES/WGS mutation calls were scored using 66 BMP-like DEGs and 53 T-speci ed DEGs using AUCell 21 v1.12.0.For 1,490 mutant genes in 110 ETP samples, the number of samples carrying mutations was quanti ed and the mean BMP-like AUC and T-speci ed AUC were calculated.Mutant genes observed in >= 5 samples and mean VAF > 0.05 were plotted for visualization.Classi cation of genes were derived from previous bulk genomics study on ETP-ALL 22 .
For fusion drivers (15 fusion drivers out of 49 unique fusions) in 110 ETP samples, the number of samples harboring each fusion driver was quanti ed.For each fusion driver, the mean BMP-like AUC, the mean T-speci ed AUC, the percentage of patients with EOI MRD+, and percentage of patients that died on trial, and the number of unique fusion partners was calculated.Fusion drivers observed in >= 2 samples were plotted for visualization.

Survival analyses based gene mutation status
For top T-speci ed and associated genes, 110 ETP patients with corresponding bulk WES/WGS/RNA-seq data were binarized into WT and mutant groups with comparison done by Wilcoxon rank-sum test.Survival analyses were performed using the Cox-Proportional-Hazard test on overall survival.For NOTCH1 mutated patients, similar analyses were performed by binning patients based on the number of subclonal NOTCH1 mutations: 0 (WT), 1, or 2+.To compare the fraction of tumor harboring NOTCH1 mutations, total VAF of corresponding NOTCH1 mutations was calculated for each patient, with comparison WT, 1 NOTCH1-mut, and 2+ NOTCH1-mut done by Wilcoxon rank-sum test.
Identi cation of a consensus, 17-gene BMP-like gene signature BMP-like DEGs from ETP-ALL (n=56 vs T-speci ed) and Non-ETP patients (n=445 BMP-like vs Post-Commit) were overlapped and average Log2FC calculated.17 genes with average Log2FC > 0.9 were retained as a consensus "BMP-17" signature.To contextualize BMP-17 marker genes within normal hematopoiesis, we performed AUC-based signature scoring using AUCell v1.12.0 on healthy donor scRNAseq reference maps in addition to individually plotting BMP-17 DEGs.To test the clinical signi cance of the BMP-17 signature, we performed AUC based signature scoring using AUCell v1.12.0 on bulk RNAsequenced diagnostic T-ALL samples from two independent COG trials (AALL0434, fully sequenced, n = 1051; AALL1231, partially sequenced, n = 75) using BMP-like-17 DEGs.Genes were ranked within each sample using the AuCell_buildRankings function and AUC was calculated using AUCell_calcAUC with the >80% expression (representing near-ubiquitous expression) were assigned a value of 1, whereas genes with 50%-80% expression (representing expression in the majority of blasts) were assigned a value of 0.5.
Finally, to prioritize genes that were consistently upregulated in different patients, we ranked genes with high statistical signi cance (1 = adjusted p-value < 1e-100, 0.5 = adjusted p-value < 1e-50).The sum of DE evidence (ranging from 0-3) and database evidence (ranging from 0-5) was taken to rank BMP-like DEGs for follow-up experimental studies and for visualizations.
In vitro drug screening with leukemia active drug panel Human leukemia blasts were collected from mouse spleen and enriched using immunomagnetic isolation kit (Stemcell Technologies, #19849) and screened with a panel of 40 leukemia active drugs (Supplementary Table 18) using a previously described imaging-based assay with a stromal cell coculture system 29 .
Nomination of BMP-like speci c drugs from in drug screening PDX expanded blasts from 5 patients were screened in a stromal co-culture system and dose response curves were generated for each compound with the primary readout being cell viability (% of control).We de ned ETP active drugs as compounds with IC50 < 1000nM and categorized each compound as not active (active in 0/4 ETP patients), partially active (1-3 ETP patients), or active (4/4 ETP patients).We then compared IC50 values for ETP active compounds and utilized three comparisons to nominate drugs that were differentially active in High BMP patients: ETP BMP-high/MRD+ (n=3) vs BMP-low/MRD-(n=2, one ETP, one Non-ETP); BMP-high/HighMRD (n=2) vs ETP BMP-low (n=1); BMP-high/MRD+ (n=3) vs ETP BMP-low (n=1).Drugs with differential activity in BMP-high patients in all three comparisons were nominated as BMP-speci c drugs.

Declarations Figures
, Supplemental Data Fig 4b), consistent with their known essentiality in T-cell maturation.To ask whether TCF7 and LEF1 have an analogous, T-lineage speci c function in T-ALL blasts, we computationally inferred the regulators and targets of TCF7 and LEF1 in ETP, Near-ETP and Non-ETP T-ALL by constructing subtype-speci c transcriptional regulatory networks.Our analysis predicts the repression of TCF7/LEF1 expression by stem-related TFs (ie, MEF2C, IRF1, and LYL1) in ETP-ALL, and activation of TCF7/LEF1 by core TFs of T-cell commitment (ie, BCL11B, SIX6, and TCF7L2) in Near-ETP T-ALL (Extended Data Fig. 2d-e) and Non-ETP T-ALL (Supplementary Data Fig 4c) AALL0434 strati cation using BMP-119 signature BMP-like and T-speci ed DEGs stringently ltered using FDR < 0.001, average Log2FC > 0.5 cutoffs, leaving 66 BMP-like DEGs and 53 T-speci ed DEGs.Z-score based signature scoring was performed on 113 bulk-sequenced ETP-ALL patients with BMP-like DEGs as positive features, and T-speci ed DEGs as negative features.For each patient, the mean T-speci ed-feature Z-score was subtracted from the mean BMP-like-feature Z-score, with a score of >0 being interpreted as BMP-like > T-speci ed.Association of EFS and OS with BMP-like signature was assessed by two methods.First, patients were binarized by EFS/OS status (event vs no event; dead vs alive) and signature scores compared using Wilcoxon ranksum test.Second, patients were binarized by BMP-like signature score (BMP-like > T-speci ed vs Tspeci ed > BMP-like) and OS and EFS compared using the Cox Proportional Hazard model with Day 29 MRD and CNS status taken as covariates using the surv t function from survival 3.2-13: surv t(Surv(time.survival,status.survival)~ high.BMP + D29.MRD + D29.CNS.Status.

Figure 2 Treatment
Figure 2

Figure 4 A
Figure 4